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arXiv 2104.03167stat.MEmath.COstat.AP
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对数线性 ERGM 的拟合优度

Goodness of fit for log-linear ERGMs

Elizabeth Gross, Sonja Petrović, Despina Stasi

AI总结:

本文提出融合节点与群体效应的 p1-SBM,并基于 Markov 基动态自适应抽样构建 log-linear ERGM 的精确条件拟合优度检验,在两个生物网络上验证其性能与可扩展性。

摘要:

网络文献中许多常用模型都可以通过网络二元组上的列联表这一统一视角来看待,由此得到 log-linear ERGMs(对数线性指数随机图模型):即随机图的指数族模型,其充分统计量在二元组上是线性的。我们在该模型族中提出了一个新模型 p1-SBM,它结合了网络形成机制中常见的节点效应和群体效应。特别地,它是若干著名 ERGM 的推广,包括具有已知块分配的无向图随机块模型、其度校正版本,以及没有群体结构的有向 p1 模型。我们通过一个精确条件检验来构建 log-linear ERGM 类的模型拟合检验问题,其 p 值可以在小型和中等规模网络中得到高效近似。我们构建的抽样方法依赖于 Markov 基的动态自适应。我们使用改编自列联表文献的快速估计算法,以及植根于图论和代数统计学的有效抽样方法。该方法的性能和可扩展性在两个生物学数据集上得到展示:秀丽隐杆线虫(C. elegans)的连接组和拟南芥(Arabidopsis thaliana)的相互作用组。这两个网络——一个神经网络和一个蛋白质-蛋白质相互作用网络——一直是网络科学文献中的常用例子。我们的工作为研究它们提供了一种基于模型的方法。

英文摘要:

Many popular models from the networks literature can be viewed through a common lens of contingency tables on network dyads, resulting in \emph{log-linear ERGMs}: exponential family models for random graphs whose sufficient statistics are linear on the dyads. We propose a new model in this family, the \emph{$p_1$-SBM}, which combines node and group effects common in network formation mechanisms. In particular, it is a generalization of several well-known ERGMs including the stochastic blockmodel for undirected graphs with known block assignment, the degree-corrected version of it, and the directed $p_1$ model without group structure. We frame the problem of testing model fit for the log-linear ERGM class through an exact conditional test whose $p$-value can be approximated efficiently in networks of both small and moderately large sizes. The sampling methods we build rely on a dynamic adaptation of Markov bases. We use quick estimation algorithms adapted from the contingency table literature and effective sampling methods rooted in graph theory and algebraic statistics. The performance and scalability of the method is demonstrated on two data sets from biology: the connectome of \emph{C. elegans} and the interactome of \emph{Arabidopsis thaliana}. These two networks -- a network and a protein-protein interaction network -- have been popular examples in the network science literature. Our work provides a model-based approach to studying them.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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